Hybrid Artificial Bee Colony Research Articles

Two approaches for the min-degree constrained minimum spanning tree problem

Given an undirected, connected and weighted graph, the min-degree constrained minimum spanning tree (md_MST) problem aims to find a spanning tree (T) with minimum cost subject to each non-leaf vertex in T must have at least d degree, where d is a constant positive integer. Being a NP-hard problem, it finds several practical applications in the design of networks. In this paper, we present two approaches for this problem in which the first approach is a hybrid metaheuristic technique (hABC) combining an artificial bee colony algorithm with local search, and the second approach is iterated local search (ILS). The proposed hABC distinguishes itself from the existing hybrid artificial bee colony algorithm particularly by its size of employed bee population, initial solution generation, neighborhood operators, the way scout bee phase is handled and the local search. The proposed hABC works with a population of only 3 solutions that effectively coordinates with other key components of hABC and helps in finding new high quality solutions particularly for larger size of available benchmark instances. Extensive experiments on four data-sets of 105 benchmark instances demonstrate that ILS and hABC dominate state-of-the-art approaches. Specifically, hABC finds new best values for 41 instances, whereas ILS finds new best values for 28 instances in comparison to state-of-the-art approaches. Experimental results also show that hABC without local search (hABC-LS) is overall superior to state-of-the-art approaches except one where hABC-LS is comparable, but computationally faster on available benchmark instances.

Solving maximum quasi-clique problem by a hybrid artificial bee colony approach

Given a graph and a threshold (γ), the maximum quasi-clique (MQC) problem is to find a maximum cardinality subset of vertices in the graph such that the edge density of a corresponding induced subgraph is not less than γ. This problem istended version of the famous maximum clique problem, which arises in a various application domains and is known as NP-hard. In this study, we propose a heuristic method called the hybrid artificial bee colony (HABC) algorithm to address the MQC problem by incorporating several dedicated strategies into the artificial bee colony framework, which starts with an opposition-based initialization phase and then repeatedly alternates between an employed bees phase, an onlooker bees phase and a scout bees phase to perform the search. The employed bees phase and onlooker bees phase employ a decent local search and solution-based tabu search to locate the local optima and intensify the search,whereas the scout bees phase employs an opposition-based mechanism to reconstruct the best-found solutions to escape from the local optima and diversify the search. Experimental results indicate that our proposed HABC algorithm can improve the best-known solutions for 46 out of 112 problem instances and match the best-known solutions for 63 instances. Comparisons with the state-of-the-art heuristics and exact methods demonstrate the merits of the proposed algorithm.

An artificial bee colony algorithm for scheduling call centres with weekend-off fairness

The workforce scheduling problem in call centres is defined as assigning a number of employees to various overlapping shifts during a specified planning horizon, considering some regulations and preferences, so as to minimize the labour cost. This is often difficult to solve to optimality due to its combinatorial structure. In this paper, we propose an enhanced artificial bee colony (EABC) algorithm to solve the problem. After introducing a good-quality solution generated by a heuristic algorithm to the initial population, the solutions are further exploited by four elaborate neighbourhood structures, which are designed based on the decomposed structure of the problem and deeply embedded in the solution evolution mode. Furthermore, a modified abandoning mode is employed in the onlooker stage. Using real instances from Chengxi Call Centre in Suqian, China, the experimental results show that the proposed algorithm can achieve (sub-)optimal solutions for large-scale problems. Furthermore, a comparative evaluation of EABC is carried out against a hybrid artificial bee colony (HABC) and simulated annealing (SA). The results show that EABC outperforms the other two algorithms for all problem instances. In addition, we analyse the influence of the weekend-off fairness on the labour cost.

Hybrid swarm intelligence-based software testing techniques for improving quality of component based software

Being a time-consuming and costly activity, software testing always demands optimization and automation. Software testing is an important activity to achieve quality and customer satisfaction. This paper presents a comparative evaluation of different hybrid automated software testing techniques using the concepts of soft computing for overall quality enhancement. A comparison between three hybrid automation techniques is carried out i.e., hybrid ant colony optimization-genetic algorithms (ACO-GA), hybrid artificial bee colony (ABC)-Naïve Bayes, hybrid ABC-GA along with three parent approaches. The comparison is made by applying these hybrid techniques for the selection of minimized test suites thus reducing overall testing effort and eliminating useless or redundant test cases. The experimental results prove the efficiency of these hybrid approaches in different scenarios. The impact of automated testing techniques for quality enhancement is assessed in terms of defect density and defect detection percentage.

A hybrid artificial bee colony with whale optimization algorithm for improved breast cancer diagnosis

Breast cancer is the most common among women that leads to death if not diagnosed at early stages. Early diagnosis plays a vital role in decreasing the mortality rate globally. Manual methods for diagnosing breast cancers suffer from human errors and inaccuracy, and consume time. A computer-aided diagnosis (CAD) can overcome the disadvantages of manual methods and helps radiologists for accurate decision-making. A CAD system based on artificial neural network (ANN) optimized using a swarm-based approach can improve the accuracy of breast cancer diagnosis due to its strong prediction capabilities. Artificial bee colony (ABC) and whale optimization are metaheuristic search algorithms used to solve combinatorial optimization problems. This paper proposes a hybrid artificial bee colony with whale optimization algorithm (HAW) by integrating the exploitative employee bee phase of ABC with the bubble net attacking method of whale optimization to propose an employee bee attacking phase. In the employee bee attacking phase, employee bees use exploitation of humpback whales for finding better food source positions. The weak exploration of standard ABC is improved using the proposed mutative initialization phase that forms the explorative phase of the HAW algorithm. HAW algorithm is used in simultaneous feature selection (FS) and parameter optimization of an ANN model. HAW is implemented using backpropagation learning that includes resilient backpropagation (HAW-RP), Levenberg–Marquart (HAW-LM) and momentum-based gradient descent (HAW-GD). These hybrid variants are evaluated using various breast cancer datasets in terms of accuracy, complexity and computational time. HAW-RP variant achieved higher accuracy of 99.2%, 98.5%, 96.3%, 98.8%, 98.7% and 99.1% with low-complexity ANN model when compared to HAW-LM and HAW-GD for WBCD, WDBC, WPBC, DDSM, MIAS and INbreast, respectively.

Effects of memory and genetic operators on Artificial Bee Colony algorithm for Single Container Loading problem

The Artificial Bee Colony (ABC) algorithm is widely used to achieve optimum solution in a short time in integer-based optimization problems. However, the complexity of integer-based problems such as Knapsack Problems (KP) requires robust algorithms to avoid excessive solution search time. ABC algorithm that provides both the exploitation and the exploration approach is used as an alternative approach for various KP problems in the literature. However, it is rarely used for the Single Container Loading problem (SCLP) which is an important part of the transportation systems. In this study, the exploitation and exploration aspects of the ABC algorithm are improved by using memory mechanisms and genetic operators to develop three different hybrid ABC algorithms. The developed algorithms and the basic ABC algorithm are applied to a SCLP dataset from the literature to observe the effects of the memory mechanism and the genetic operators separately. Besides, a joint hybrid ABC algorithm using both reinforcement approaches is proposed to solve the SCLP. The results show that the joint hybrid ABC algorithm has emerged as a promising approach to solving SCLP with an average performance, and the genetic operators are more effective than the memory mechanism to develop a hybrid ABC algorithm.

A Combined Traffic and Workload-aware Optimized Virtual Machine Migration in Cloud Computing

In cloud computing paradigm, Virtual Machine (VM) migration strategies play a vital role in reducing the Energy Consumption (EC) and balancing the workloads in the cloud servers. From this viewpoint, an Osmotic Hybrid artificial Bee and Ant Colony with Future Utilization Prediction and Multipath Traffic Routing (OH-BAC-FUP-MTR) strategy was designed to accomplish effective load-balancing and minimize the chance of traffic congestion during VM migration in data centres. But, the tradeoffs among workload efficiency and power-gain in heterogeneous cloud servers were not analyzed. Hence in this article, a VM consolidation strategy is proposed with OH-BAC-FUP-MTR to switch the idle Physical Machines (PMs) into hibernation mode, resulting in very less EC. In this strategy, the VM migration is targeted at consolidating the VMs depending on the workload to the smaller number of PMs for reducing the power use and encouraging green computing. Initially, the PMs are split into various groups depending on their workload levels and then a new Merge-and-Split-based Coalitional Game-theoretic (MSCG) method is applied to select associates from these groups to create efficient coalitions. After that, OH-BAC-FUP-MTR is performed amongst the coalition associate for maximizing the reward of each coalition and PMs are maintained to operate in the maximum power-efficient condition. Finally, the investigational outcomes exhibit the OH-BAC-FUP-MTR-MSCG achieves a mean EC of 60.63KWh which is 28.87% less than all other classical VM migration strategies.

Application of multi-objective hybrid artificial bee colony with differential evolution algorithm for optimal placement of microprocessor based FACTS controllers

Increasing electricity demand, financial concerns for building new power stations have resulted in overloading, extreme power transfer, huge losses, reduced power eminence, consistency issues and voltage contour issues. To overcome these issues Flexible AC Transmission System (FACTS) controllers can be integrated into the transmission systems. Subsequently, it improves stagnant and vigorous enactment. Conversely, site, type and capacity of FACTS controllers have to be optimized to attain the aforesaid objectives. In this study, multiple objectives such as minimization of voltage deviation, losses, cost, and line loading index have been optimized using hybrid Artificial Bee Colony-Differential Evolution (ABC-DE). Three microprocessor controlled FACTS controllers for instance thyristor controlled series compensator (TCSC), static VAR compensator (SVC) and unified power flow controller (UPFC) have been placed optimally on IEEE 30 bus system. The outcomes of hybrid ABC-DE have been validated with the outcomes of particle swarm optimization (PSO). It implies that hybrid ABC/DE outperformed PSO for allocating FACTS controllers by optimizing multiple objectives concurrently.

Resource utilization prediction with multipath traffic routing for congestion-aware VM migration in cloud computing

Background: The Load Balancing (LB) schemes in cloud computing consider both current and future utilization of resources to decide the most suitable Virtual Machines (VMs) to be migrated to the most appropriate Physical Machines (PMs). But, the possibility of network congestion occurrence was high when increasing the bandwidth use between VMs within the cloud data centers. Also, a less-than-optimal migration of VMs can lead to high network traffic since it causes inter-VM traffic for traversing the bottleneck network routes. Objective: To enhance the efficiency of LB and reduce the possibility of congestion occurrence during VM migration in cloud data centers. Methods: Osmotic Hybrid artificial Bee and Ant Colony with Future Utilization Prediction with Multipath Traffic Routing (OH-BAC-FUP-MTR) mechanism are presented in this article to achieve the above objective. Initially, the OH-BAC-FUP mechanism is performed to decide the most suitable VMs to be migrated to the most suitable PMs. During VM migration, if any congestion exists due to high bandwidth use or traffic flows, then the MTR algorithm is applied to partition the flows and route them through multiple link-disjoint routes. Based on this, the congestion is avoided while ensuring the bandwidth and security grade demands. Also, the highest traffic on the path is applied as a congestion factor. Moreover, the current and future network states are taken into account for MTR to select the most optimal route from multiple routes with no consideration of the past use of the paths. Findings: The simulation outcomes demonstrate the OH-BAC-FUP-MTR mechanism consumes 8.74% of overall energy, 6.8% of Service Level Agreement violation Time per Active Host (SLATAH), 27.63% of Performance Degradation due to Migration (PDM), 22.98% of SLA Violation (SLAV), 27.27% of VM migrations and 30.77% of hosts shutdown compared to the OH-BAC-FUP using Linear Regression (LR) and Optimal Piecewise LR (OPLR). Keywords: Cloud computing; load balancing; VM migration; OH-BAC-FUP; multipath traffic routing

A hybrid artificial bee colony algorithm for scheduling of digital microfluidic biochip operations

SummaryDigital microfluidic biochips (DMFBs) are designed to efficiently carry out biochemical and biomedical analysis in a miniaturized way. DMFBs offer various advantages over traditional laboratory techniques and reduces cost, and increases automation and software programmability. Scheduling of microfluidic operations is the first and essential step in the fluidic‐level synthesis of DMFBs, while the other two are the module placement and droplet routing. Scheduling DMFB operations is a multiconstrained optimization problem, and the particular decision problem is NP‐complete. We propose a hybrid artificial bee colony (ABC) algorithm using generalized N‐point crossover (GNX) based scheduling of DMFB operations. Proposed ABC‐GNX perturbs through search space, evaluates various schedules possible, and returns the best schedule among the evaluated schedules. Simple list scheduling based heuristic algorithms can explore a single schedule based on the sequence generated by the priority function. Iterative improvement based search algorithms explore the search space and evaluate more schedules, but the proposed ABC‐GNX algorithm produces optimal solutions in shorter execution times. Simulation results show that the proposed ABC‐GNX produces a higher number of optimal completion times and faster execution times than existing algorithms.

Application Research for Multiobjective Low-Carbon Flexible Job-Shop Scheduling Problem Based on Hybrid Artificial Bee Colony Algorithm

A hybrid artificial bee colony (HABC) solved the multiobjective low-carbon flexible job-shop scheduling problem (MLFJSP) was proposed in the paper. HABC algorithm uses a two-layer coding method to establish the initial population as the nectar source for the employed bees. In the optimization process, the employed bee phase and the onlooker bee phase adopt improved crossover mutation strategies and adaptive neighborhood search strategies to generate new nectar sources, and the greedy method is used to retain better solutions. The scout bee update mechanism prevents the algorithm from falling into a local optimum and enhances the convergence of the algorithm. In order to prevent the loss of the optimal solution, the optimization results of each phase are saved in the Pareto archive (PA). Finally, two sets of international standard instances are used to carry out simulation experiments. The simulation results demonstrated that HABC algorithm is an effective algorithm to solve the multiobjective low-carbon flexible job shop scheduling problem.

Prediction Model for Cardiovascular Disease Risk in Type2 Diabetic Patients Using a Hybrid Artificial Bee Colony Model and Semi-Supervised Learning

Prediction Model for Cardiovascular Disease Risk in Type2 Diabetic Patients Using a Hybrid Artificial Bee Colony Model and Semi-Supervised Learning

Join query optimisation in the distributed databases using a hybrid harmony search and artificial bee colony algorithm

Join query optimisation in the distributed databases using a hybrid harmony search and artificial bee colony algorithm

A hybrid artificial bee colony algorithmic approach for classification using neural networks

A hybrid artificial bee colony algorithmic approach for classification using neural networks

A Biologically Inspired Algorithm for Low Energy Clustering Problem in Body Area Network

The growing application of body area networks (BANs) in different fields makes the low energy clustering a paramount issue. A clustering optimization algorithm in BANs is a fundamental scheme to guarantee that the essential collected data can be forwarded in a reliable path and improve the lifetime of BANs. Low energy clustering is a technique, which provides a method that shows how to reduce network communication costs in BANs. A careful low energy clustering scheme is one of the most critical means in the research of BANs, which has attracted considerable attention, comprising monitoring capability constraints. However, the classical clustering method leads to high cost when constraints such as large overall energy consumption are undertaken. Hence, a binary immune hybrid artificial bee colony algorithm (BIHABCA), a randomized swarm intelligent scheme applied in BANs, motivated by immune theory and hybrid scheme is introduced. Furthermore, we designed the formulation that considers both distances between two nodes and the length of bits. Finally, we have compared the energy cost optimized by BIHABCA with a shuffled frog leaping algorithm, ant colony optimization, and simulated annealing in the simulation with different quantity of nodes in terms of energy cost. Results show that the energy cost of the network optimized by the proposed BIHABCA method decreased compared to those by the other three methods which mean that the proposed BIHABCA finds the global optima and reduces the energy cost of transmitting and receiving data in BANs.

Hybrid Harmony Search Differential Evolution Algorithm

Differential evolution (DE) algorithm has some excellent attributes including strong exploration capability. However, it cannot balance the exploitation with exploration ability in the search process. To enhance the performance of the DE algorithm, this paper proposes a new algorithm named hybrid harmony differential evolution algorithm (HHSDE). The key features of HHSDE algorithm are as follows. First, a new mutation operation is developed for improving the efficiency of mutation, in which the New Harmony generation mechanics of the harmony algorithm (HS) is employed. Second, the harmony memory size is updated with the iteration. Third, a self-adaptive parameter adjustment strategy is presented to control scaling factor. Fourth, a new evaluation method is proposed to effectively assess the algorithm convergence performance. Two classical DE algorithms, HS algorithm, improvement Differential evolution algorithm(ISDE) and Hybrid Artificial Bee Colony algorithm with Differential Evolution(HABCDE) have been tested against HHSDE based on 25 benchmark functions of CEC2005 and the results reveal that the proposed algorithm is better than the other algorithms under consideration.

Resource Utilization Prediction with Multipath Traffic Routing for Congestion-aware VM Migration in Cloud Computing

[Load Balancing (LB) is the most essential challenge in cloud computing for assigning the workload equally among each node that avoids the state of nodes being overloaded when others are under-loaded. To tackle this challenge, an Osmotic Hybrid artificial Bee and Ant Colony with Future Utilization Prediction (OH-BAC-FUP) algorithm has been proposed that considers both current and future utilizations of resources for choosing the most suitable Virtual Machines (VMs) to be migrated to the most appropriate Physical Machines (PMs). On the other hand, the possibility network congestion occurrence was high when increasing the bandwidth use between VMs within the cloud data centers. Also, the resource utilization efficiency was degraded when the number of congestions was high. A less-than optimal migration of VMs can lead to high network traffic since it causes inter-VM traffic for traversing the bottleneck network routes. Therefore in this article, OH-BAC-FUP with Multipath Traffic Routing (OH-BAC-FUP-MTR) algorithm is proposed to increase the efficiency of LB and minimize the possibility of congestion occurrence in cloud data centers. Initially, OH-BAC-FUP algorithm is performed for choosing the most appropriate VMs to be migrated to the most suitable PMs. If any congestion exists due to high bandwidth use or traffic flows, then MTR algorithm is applied to partition the flows and route them through multiple link-disjoint routes. Based on this, the congestion is avoided while ensuring the bandwidth and security grade demands. Also, the maximum load on any link is applied as a measure of congestion. Moreover, the current and future network states are taken into account for MTR to select the most optimal route from multiple routes without considering the past utilization of the links. Finally, the simulation outcomes demonstrate the efficiency of OH-BAC-FUP-MTR algorithm compared to the OH-BAC-FUP. ]

Component mixing with a cold standby strategy for the redundancy allocation problem

This study discusses the combination of cold standby strategy (CS) and component mixing (CM), proposes a new model RRAP-CM-CS to improve system reliability. The core technology used is a hybrid artificial bee colony algorithm (ABC) and simplified swarm optimization (SSO) search method. RRAP-CM-CS itself uses an exponential parameter as a decision variable to determine the reliability of each component. In addition, the number of components used by a subsystem must also be optimized, so the problem becomes more complicated, requiring a more diverse solution for system reliability. Experiment results verify that the proposed method not only reduces costs, but also significantly improves the use of weight, volume and computing time, which improves system reliability.

Optimization for cooperative task planning of heterogeneous multi-robot systems in an order picking warehouse

This article investigates the cooperative task planning problem of heterogeneous multi-robot systems in an order picking warehouse, with challenging characteristics such as heterogeneous agents, interrelated utilities and complex intertask dependencies. These characteristics bring great challenges for modelling and optimization of the problem. This study establishes a novel mapping mechanism to describe the problem as open shop scheduling with sequence-dependent set-up and transportation times, and develops an efficient mixed-integer linear programming model to obtain the optimal task planning scheme for small-size problems. To increase the size of solvable problems and improve the performance of the solution, an effective hybrid artificial bee colony algorithm is proposed, which introduces some concepts from the tabu search algorithm and the artificial immune algorithm. The effectiveness of the proposed method is confirmed by simulation experiments.

A new and robust hybrid artificial bee colony algorithm – ANN model for FRP-concrete bond strength evaluation

The present study describes the bond strength estimation between the FRP strip and the substrate concrete using machine learning approaches. To provide a precise model, a database including 656 single and double-lap direct shear test results was compiled from the literature. Artificial neural networks (ANN) and the combination of ANN with the artificial bee colony optimization algorithm (ABC-ANN) were implemented for the model development. The concrete compressive strength, the elasticity modulus of FRP strip, FRP thickness, the width of the FRP strip, and the width of concrete block were considered as input parameters of the models. The results of the ANN and the hybrid ABC-ANN were compared with those of existing models and international procedures. It was indicated that the accuracy of the proposed models outperforms the existing models. The correlation coefficients of the ABC-ANN and ANN approaches are 0.97 and 0.93, respectively. The robustness of the models was investigated using the distribution of absolute error values. It was demonstrated that the proposed ABC-ANN model is robust and resulted in the least error values, distributed in small ranges (less than 10%), compared to ANN and other methods. Using the hybrid ABC-ANN, a straightforward formulation for bond strength evaluation was proposed.